The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China.
Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China.
Int J Environ Res Public Health. 2020 Apr 3;17(7):2437. doi: 10.3390/ijerph17072437.
Roads should deliver appropriate information to drivers and thus induce safer driving behavior. This concept is also known as "self-explaining roads" (SERs). Previous studies have demonstrated that understanding how road characteristics affect drivers' speed choices is the key to SERs. Thus, in order to reduce traffic casualties via engineering methods, this study aimed to establish a speed decision model based on visual road information and to propose an innovative method of SER design. It was assumed that driving speed is determined by road geometry and modified by the environment. Lane fitting and image semantic segmentation techniques were used to extract road features. Field experiments were conducted in Tibet, China, and 1375 typical road scenarios were picked out. By controlling variables, the driving speed stimulated by each piece of information was evaluated. Prediction models for geometry-determined speed and environment-modified speed were built using the random forest algorithm and convolutional neural network. Results showed that the curvature of the right boundary in "near scene" and "middle scene", and the density of roadside greenery and residences play an important role in regulating driving speed. The findings of this research could provide qualitative and quantitative suggestions for the optimization of road design that would guide drivers to choose more reasonable driving speeds.
道路应该向驾驶员提供适当的信息，从而诱导更安全的驾驶行为。这一概念也被称为“自解释道路”（SER）。先前的研究表明，理解道路特征如何影响驾驶员的速度选择是 SER 的关键。因此，为了通过工程方法减少交通事故，本研究旨在建立基于视觉道路信息的速度决策模型，并提出一种 SER 设计的创新方法。研究假设驾驶速度由道路几何形状决定，并受环境影响进行修正。车道拟合和图像语义分割技术被用于提取道路特征。在中国西藏进行了现场实验，共选取了 1375 个典型的道路场景。通过控制变量，评估了每条信息所激发的驾驶速度。使用随机森林算法和卷积神经网络构建了几何决定速度和环境修正速度的预测模型。结果表明，“近景”和“中景”中右侧边界的曲率，以及路边绿化和住宅的密度，对调节驾驶速度起着重要作用。本研究的结果可为道路设计的优化提供定性和定量的建议，引导驾驶员选择更合理的驾驶速度。
